Journal of Economics, Finance and Accounting Studies
Founded in 2019, the Journal of Economics, Finance and Accounting Studies (JEFAS) is a double-blind peer-reviewed, open-access monthly journal published by Al-Kindi Center for Research and Development. The journal is led by expert international editorial board members who take an objective approach to peer review, ensuring each research paper is reviewed and evaluated on its own scholarly merits and research integrity. Publishing your work in the Journal of Economics, Finance and Accounting Studies means you will receive prompt and reliable publishing and a global platform for your research to reach its full potential. The journal is published in online version only.
Al-Kindi Center for Research and Development
Coretax in Practice: How Tax Consultants Perceive Digital Tax Reporting Effectiveness
This study aims to analyze the effectiveness of the tax reporting process in the Core Tax Administration System (Coretax) from the perspective of tax consultants in 2025. The study employs a quantitative approach by integrating the DeLone and McLean Information System Success Model and the Technology Acceptance Model (TAM). Data were collected through a questionnaire administered to 250 tax consultants/Coretax users and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The variables examined comprise system quality, information quality, service quality, perceived ease of use as a mediating variable, and tax reporting effectiveness as the dependent variable. The instrument test results indicate that all items are valid and all constructs are reliable, with Cronbach's Alpha values ranging from 0.740 to 0.933, Composite Reliability values from 0.834 to 0.952, and AVE values from 0.504 to 0.799. The structural model shows that system quality, information quality, and service quality positively affect perceived ease of use. System quality and information quality also significantly affect tax reporting effectiveness, whereas service quality does not have a significant direct effect. Perceived ease of use is proven to mediate the effects of system quality and information quality on tax reporting effectiveness. The Importance-Performance Map Analysis (IPMA) indicates that system quality is the most important factor to be maintained, while information quality represents the most realistic improvement area for organizational management. These findings contribute to the literature on tax digitalization and provide practical recommendations for Coretax system managers in improving the quality of tax reporting.
Coretax; system quality; information quality; service quality; perceived ease of use; tax reporting effectiveness
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The Evolving Role of Treasury Systems: How Banks Can Outpace Fintechs
Corporate treasury management sits at the intersection of finance, technology, and institutional trust, and it is changing fast. Over the past decade, financial technology companies have reshaped how corporations initiate payments, monitor balances, and manage relationships with multiple banking partners. Their appeal is understandable: fintech platforms are faster to deploy, more intuitive to use, and often far less bureaucratic than the legacy systems offered by traditional banks. But beneath the polished interfaces and compelling value propositions, a structural problem persists. Most fintech treasury solutions are built for one-directional communication, they carry corporate instructions outward to banks but struggle to bring meaningful, real-time feedback back. Transaction confirmations are delayed. Reconciliation is partially manual. Exception handling depends on workarounds. These are not minor inconveniences; for treasury teams managing millions of dollars across multiple currencies and jurisdictions, they represent genuine operational and financial risk. This paper examines that gap and argues that it represents a significant strategic opportunity for traditional banks. Banks are not simply competing with fintechs, they are positioned, structurally and institutionally, to offer something fintechs cannot: a fully integrated, bidirectional treasury ecosystem built on the foundations of regulatory authority, settlement infrastructure, balance sheet depth, and decades of corporate relationship capital. Through a comprehensive review of academic literature and industry practice published between 2016 and 2024, this study develops the Bank Strategic Advantage Framework (BSAF), a five-dimensional model that maps the specific areas where banks hold durable competitive advantages over fintech competitors in the corporate treasury space. The paper further proposes a platform architecture model incorporating Banking-as-a-Service (BaaS) and Channel-as-a-Service (CaaS) frameworks, through which banks can operationalize these advantages into treasury products that rival and surpass current fintech offerings. The findings have practical implications for bank executives, treasury professionals, and policymakers seeking to understand the evolving competitive dynamics of corporate financial services.
treasury management systems, fintech disruption, corporate banking, Banking-as-a-Service, Channel-as-a-Service, bidirectional integration, payment platforms, digital transformation, cash management, SWIFT
Strengthening U.S. Global Leadership in Electric Vehicle Supply Chains Through AI-Driven Fintech Innovation: A Comprehensive Interdisciplinary Analysis
This study examines how artificial intelligence (AI) and fintech innovation can strengthen U.S. leadership in electric vehicle (EV) supply chains. As global competition intensifies among major economies—particularly the United States, China, and the European Union—the United States must enhance supply-chain resilience, battery sourcing security, infrastructure financing, and advanced manufacturing competitiveness. The paper proposes a comprehensive AI-driven fintech framework integrating five core pillars: predictive analytics, blockchain traceability, digital twins, embedded finance, and intelligent risk management. Drawing upon recent interdisciplinary research in AI applications, healthcare analytics, supply-chain optimization, EV adoption behavior, charging infrastructure deployment, and financial transformation, the study argues that AI-enabled financial ecosystems can substantially improve capital allocation, operational efficiency, sustainability performance, and national competitiveness. The findings suggest that coordinated investments in AI, clean energy technologies, fintech infrastructure, and advanced manufacturing can reinforce U.S. economic leadership while simultaneously accelerating the global transition toward sustainable mobility. Policy recommendations, industry implications, and future research directions are discussed.
Artificial intelligence, electric vehicles, fintech, supply chain, blockchain, digital twin, charging infrastructure, U.S. competitiveness
Healthcare System Sustainability for an Aging Population in the United States: Economic Challenges and Policy Pathways
Population aging is one of the major long-term economic challenges facing the U.S. healthcare system. As life expectancy increases and the older-adult population expands, demand is rising for chronic disease management, hospital care, prescription medication, rehabilitation, home care, long-term services and supports, and caregiver assistance. Existing studies have examined aging-related healthcare costs, long-term care financing, medical expenditure burden, workforce shortages, and family caregiving, but these issues are often analyzed separately rather than as one integrated economic sustainability problem. This paper examines the economic sustainability of U.S. healthcare systems serving an aging population by focusing on three major pressures: increasing healthcare demand, long-term care financing strain, and healthcare delivery-capacity constraints. Drawing on peer-reviewed studies, government reports, and policy analyses published before 2025, the paper identifies a gap in the literature: limited integration of Medicare and Medicaid financing, workforce capacity, chronic disease burden, care fragmentation, unpaid caregiving, preventive care, and home- and community-based services into a single sustainability framework. The paper proposes a conceptual framework that links cost control, access, workforce development, technology-enabled care coordination, long-term care reform and caregiver support. The findings suggest that economic sustainability cannot be achieved through increased healthcare spending alone. Instead, the U.S. healthcare system must shift from a reactive, hospital-centered model toward a preventive, coordinated, technology-supported, home and community-based model that improves affordability, access, quality, and aging-care outcomes.
Aging population, Healthcare sustainability, Medicare, Medicaid, Long-term care, Economic burden, United States, Elderly care
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Institutional Learning and the Limits of Fiscal Rules: A Public Choice Perspective
This article investigates the process of constructing fiscal discipline in Brazil from the Camata Law (Lei Complementar No. 82/1995) to the approval of the Fiscal Responsibility Law (FRL) in 2000, and examines its subsequent deterioration from 2014 onwards. It analyses the evolution of personnel expenditure limits, sanction mechanisms and enforcement instruments over the period 1995 to 2000. Using a qualitative approach based on documentary and institutional analysis, the study draws on the relevant legislation and historical fiscal data series. The paper identifies the initial lack of effective sanctions and enforcement tools in the Camata Law, as well as the advances introduced by Constitutional Amendment No. 19/1998 and the New Camata Law (Lei Complementar No. 96/1999). This work shows how, through learning‑by‑doing, these successive reforms culminated in a broader institutional framework with the FRL and it concludes that fiscal discipline depends on societal pressure.
Copyright (c) 2026 Journal of Economics, Finance and Accounting Studies https://creativecommons.org/licenses/by-nc/4.0
Market Turbulence and Financial Contagion in Emerging Economies: A Meta-Analysis
Quantitative syntheses of the financial contagion literature are scarce, with frontier markets at the edge of the emerging-market category particularly under-represented. We meta-analyse 312 effect sizes from 87 studies (2002–2024) spanning 26 economies. A three-level random-effects model with cluster-robust variance estimation and Hartung–Knapp–Sidik–Jonkman adjustment returns a pooled standardised contagion coefficient of 0.187 (95% CI: 0.156–0.218; I-squared = 76.4%; tau-squared = 0.022). Multilevel meta-regression attributes 28% of between-study variance to methodological choice: DCC-GARCH and TVP-VAR connectedness measures exceed the Forbes–Rigobon adjustment by 35–45%, consistent with heteroscedasticity-induced bias. Regional moderators diverge sharply: the BRICS bloc registers 0.241, against 0.124 for Central Asia and Mongolia (k = 14), the lowest sub-sample mean. Selective-reporting diagnostics—Egger regression, trim-and-fill, PEESE, p-curve, p-uniform-star and a Vevea–Hedges selection model—indicate moderate but bounded distortion. Leave-one-out and Cook-distance diagnostics confirm that no single study drives the result. We draw three conclusions: methodological choice has first-order consequences for measured contagion; small open frontier markets exhibit attenuated direct co-movement but pronounced commodity-mediated transmission; and Central Asia and Mongolia warrant a focused research agenda calibrated to commodity- and currency-channel measurement.
Financial contagion; Emerging markets; Meta-analysis; DCC-GARCH; Mongolia; Central Asia
Data-Driven Risk Detection in Digital Financial Systems: Implications for Financial Crime Prevention and Infrastructure Resilience
The increasing digitalization of financial systems has transformed how financial institutions process transactions, deliver services, and manage operational risk. However, the rapid growth of electronic payment systems and online financial platforms has also increased exposure to fraud, identity theft, and cyber-enabled financial crime. This study explores the relationship between the expansion of the digital financial system and financial crime exposure in the United States, using publicly available data from the Federal Trade Commission (FTC) Consumer Sentinel Network and the Federal Reserve Payments Study. Adopting an exploratory and policy-oriented approach, the study uses descriptive trend analysis, comparative visualization, and conceptual financial risk interpretation to examine evolving fraud patterns and digital transaction vulnerabilities. The findings indicate that the growth of digital payment infrastructures has been accompanied by persistently high levels of fraud reports, identity theft incidents, and financial losses, particularly within credit card and electronic transaction environments. In response, the study proposes a conceptual Data-Driven Risk Detection Framework integrating transaction analytics, anomaly detection, compliance monitoring, and adaptive learning mechanisms to strengthen financial crime prevention and infrastructure resilience. The study contributes to the literature by offering a practical, infrastructure-focused perspective on data-driven financial risk governance in increasingly digital financial ecosystems.
Digital Financial Systems; Financial Crime Prevention; Fraud Detection; Financial Risk Analytics; Data-Driven Risk Detection; Financial Infrastructure Resilience
The Impact of New Quality Productive Forces on Corporate Environmental Performance: A Dual Perspective Based on Green Innovation and Environmental Information Disclosure
In the era of coordinated advancement of the “dual carbon” goals and high-quality development, how to transform new quality productive forces into tangible environmental governance outcomes has become a core issue in enterprises’ green transformation. This study takes Chinese A-share listed companies from 2015 to 2024 as the research sample and systematically examines the impact of new quality productive forces on corporate environmental performance, as well as its underlying mechanisms and heterogeneous characteristics. To address potential endogeneity issues, the instrumental variable approach and propensity score matching method are employed. The baseline regression results show that an increase in the level of new quality productive forces significantly improves corporate environmental performance. The conclusion remains robust after a series of robustness checks, including replacing the explanatory variable, changing the regression model, excluding samples from municipalities, and adjusting the clustering level. Mechanism tests reveal that new quality productive forces exert their effects primarily through two pathways: first, by stimulating enterprises’ green technological innovation vitality and promoting the output of green invention patents, thereby providing technological support for environmental improvement; second, by enhancing the quality of environmental information disclosure, which increases the transparency and supervisability of corporate environmental behavior and forms a combined internal and external supervisory force that compels continuous improvement. Heterogeneity analysis indicates that the positive environmental effect of new quality productive forces is more pronounced in enterprises with high profitability, in high-tech industries, and in heavily polluting industries, while the effect is relatively weaker in enterprises with low profitability, in non-high-tech industries, and in lightly polluting industries. This study not only theoretically expands the cognitive boundaries of the environmental effects of new quality productive forces and reveals the dual transmission mechanisms of green innovation and environmental information disclosure, but also provides practical references for enterprises to cultivate new quality productive forces in a differentiated manner according to their own resource endowments, thereby promoting green transformation.
New quality productive forces; corporate environmental performance; level of green innovation; environmental information disclosure quality
Education Outcomes and Structural Constraints in Côte d’Ivoire: A Political Economy Analysis of Human Capital Formation
This study examines the relationship between educational investment, system efficiency, and human capital formation in Côte d’Ivoire within a political economy framework. While human capital theory suggests that increased investment in education promotes productivity and economic growth, evidence from developing economies indicates that educational expansion alone does not necessarily generate improved developmental outcomes. The study aims to analyze how institutional quality, governance structures, labor market alignment, and structural constraints influence the effectiveness of educational investments in Côte d’Ivoire. The research adopts a qualitative analytical approach based on a structured review of academic literature, development reports, and policy documents from institutions including the World Bank, UNESCO, UNICEF, and the African Development Bank. The analysis integrates perspectives from human capital theory, endogenous growth theory, and institutional economics to evaluate the interaction between educational access, learning quality, and socio-economic outcomes. Findings indicate that although Côte d’Ivoire has achieved substantial progress in expanding access to education, major challenges persist in terms of learning outcomes, resource efficiency, labor market mismatch, demographic pressure, and institutional capacity. The study demonstrates that the developmental returns of education depend not only on financial investment but also on governance quality, policy coordination, and the alignment between educational systems and economic structures. The paper contributes to the literature by providing an interdisciplinary interpretation of education outcomes in a developing-country context and by emphasizing the limitations of input-centered education policies. It concludes that sustainable human capital formation requires reforms focused on education quality, institutional effectiveness, accountability, and labor market relevance rather than access expansion alone.
human capital formation; education policy; institutional efficiency; political economy; Côte d’Ivoire; governance; labor market alignment
Electrifying Healthcare Logistics: The Role of Electric Vehicle Transportation in Optimizing Pharmaceutical Value Chains, Digital Marketing, and Financial Performance
The transformation of healthcare logistics through electric vehicle (EV) adoption represents a critical advancement in optimizing pharmaceutical value chains. This study examines the integration of EV transportation systems within healthcare distribution networks and evaluates their impact on operational efficiency, digital marketing capabilities, and financial performance. By synthesizing insights from supply chain analytics, artificial intelligence, and sustainable mobility frameworks, the research identifies key drivers, barriers, and outcomes associated with electrified logistics. The findings suggest that EV-enabled logistics not only reduce environmental impact but also enhance delivery reliability, improve patient-centric service models, and support data-driven marketing strategies. Furthermore, financial analysis indicates long-term cost advantages despite high initial investment, positioning EV logistics as a strategic asset in modern healthcare ecosystems.
Electric Vehicles, Healthcare Logistics, Pharmaceutical Supply Chain, Digital Marketing, Financial Performance, Sustainability, AI Integration
Economic Resilience of Immunization Systems: Development and Validation of the ERIIS Index
The health and economic benefits of vaccination programs are tremendous, yet the financial sustainability will be vulnerable to macroeconomic crises, change of donors and government budget constraints. Current research calculates the health impact and cost-effectiveness and does not determine the economic resilience at the system level. In this paper, Economic Resilience Index of Immunization Systems (ERIIS), a composite and multi-dimensional index, is introduced; the index is designed to validate and forecast the ability of a country to maintain immunization outcomes in the case of fiscal shock. ERIIS is a composite score of five pillars: fiscal autonomy, health economic integration, procurement efficiency, funding diversification and data and forecast capacity into 100 points. According to the model, there are measurable indicators, weighting process, and a validation method that are to be employed in an empirical manner. ERIIS can provide a forecasting tool to policymakers because operationalizing economic resilience can be used to determine vulnerability, improve financial preparedness, and reform sustainable immunszation.
Economic resilience, Immunization systems, Vaccine financing, Health system resilience, Public financial management, Immunization economics, Vaccine procurement, Health policy
Retirement Trends Among PERKESO Contributors: Economic and Social Implications for Malaysia and Policy Insights from Long-term Ageing Governance Experience
Malaysia is increasingly confronting what demographers describe as a silver wave, marked by a rapid rise in the proportion of older persons driven by sustained fertility decline, rising life expectancy, and shifts in population age structure. In 2024, the Department of Statistics Malaysia estimated that individuals aged 60 years and above accounted for 11.6 per cent of the total population, or approximately 3.9 million people. Concurrently, the total fertility rate declined to 1.7 children per woman in 2023, the lowest level in half a century and well below the replacement threshold of 2.1, placing Malaysia on the trajectory toward becoming an aged nation by 2040. This study provides a comprehensive analysis of the economic and social impacts of population ageing in Malaysia, with particular emphasis on retirement trends derived from contribution data from the Social Security Organisation (PERKESO). Using administrative contribution records, the analysis examines patterns of workforce exit, continued labour participation among older adults, and implications for labour supply and social security sustainability. The findings indicate that while population ageing presents significant economic risks including a shrinking workforce, potential constraints on GDP growth, increased pension liabilities, and rising healthcare and long-term care expenditure, which also present opportunities for productivity retention and inclusive growth through extended workforce participation. Although healthcare and elderly care spending currently accounts for a relatively small share of GDP, these expenditures are expected to rise substantially by 2040 in response to age-related demands. In order to situate these empirical findings within the context of a broader policy trajectory, this empirical analysis is complemented with institutional insights drawn from long-term ageing governance experience in Belgium. From a social perspective, a growing older population intensifies dependency ratios, placing pressure on families, communities, and public welfare systems. Challenges such as social isolation and age-unfriendly urban infrastructure further heighten vulnerability among older persons. Nevertheless, the study highlights the role of employment services, retraining initiatives, and community-based support in promoting social inclusion, dignity, and financial security in later life. PERKESO contribution data further reveal a rising trend of delayed retirement, reflecting both financial necessity and a willingness among older adults to remain economically active. Since this trend supports pension sustainability but underscores the need for flexible retirement policies, extended contribution mechanisms, and broader coverage for non-standard and gig employment, Belgium is introduced as an institutional reference point. As this country has already progressed further along the demographic ageing trajectory while operating within a comparable social security and labour market governance framework as Malaysia, its experience proves an analytically relevant case for contextualising Malaysian retirement trends. In Belgium, successive reforms have reconfigured the balance between early exit pathways and active end-of-career participation. This institutional reflection underscores the importance of policy sequencing, targeted incentives, and evidence-based design in avoiding unintended consequences such as deadweight loss or substitution effects. Overall, the study underscores the urgency of repositioning older persons not merely as dependents but as productive economic and social assets. Aligning labour market policies, employment services, healthcare systems, and urban planning with demographic realities will be critical to ensuring Malaysia’s long-term economic resilience and social cohesion in an ageing society.
DATO’ SRI DR. MOHAMMED AZMAN AZIZ MOHAMMED
Adoption of Seventeen SMA Tools in the Saudi Sectors: A Contemporary Analysis
This study investigates the adoption of seventeen strategic management accounting tools (SMAT) across Saudi service sector organizations, integrating technological, organizational, and strategic determinants within a unified TOE–TAM–SMAT–Digital Capability framework. Using a quantitative, cross-sectional design, data were collected from accountants and managers representing diverse service industries, enabling a comprehensive assessment of adoption patterns and the influence of demographic and organizational characteristics. The findings reveal that competitor-oriented tools—particularly benchmarking and competitive position monitoring—are the most widely adopted, reflecting the sector’s emphasis on external market intelligence and strategic positioning. Customer oriented tools exhibit moderate adoption, with strategic pricing and customer profitability analysis leading the category, while advanced customer analytics remain underutilized. Cost management tools show selective but meaningful uptake, with value chain costing and life cycle costing demonstrating the highest usage. Demographic variables such as company size, service type, listing status, and managerial experience exert minimal influence on adoption, indicating that structural characteristics play a limited role. Instead, digital capability, SMAT orientation, and market position emerge as the primary drivers shaping adoption pathways. The moderating effect of market position strengthens the relationship between digital capability and adoption, while SMAT orientation mediates the influence of digital capability on cloud accounting usage. Reliability and validity analyses confirm the robustness of the measurement instrument. Overall, the study provides empirical evidence that SMAT adoption in the Saudi service sector is driven by strategic imperatives and digital readiness rather than demographic factors. The findings highlight the need for enhanced digital and analytical capabilities to support the adoption of advanced SMAT, particularly those requiring long term customer insight and sophisticated cost analytics. The study contributes to the understanding of SMA in digitally transforming service economies and offers practical implications for managers, policymakers, and educators seeking to strengthen strategic decision making and performance measurement practices.
Cloud Accounting; TOE Framework; TAM; SMAT Orientation; Digital Capability; Market Position; Competitive Intelligence; Strategic Agility; Cost Efficiency; Technology Adoption
Electric Vehicles and Healthcare Efficiency: A Critical Perspective on Cost, Access, and Environmental Impact
The intersection of Electric Vehicles (EVs) and healthcare efficiency presents a complex landscape where environmental sustainability, economic viability, and public health outcomes converge. While the transition to EVs is often framed as a purely environmental or automotive shift, its implications for healthcare infrastructure and patient access are profound. This paper critically examines how EV adoption in healthcare—particularly in logistics, emergency response, and patient transportation—affects cost structures, service accessibility, and sustainability outcomes. By integrating insights from digital health technologies, artificial intelligence, and evolving consumer expectations, this study highlights both the opportunities and systemic challenges of electrifying healthcare infrastructure in the United States.
Good Corporate Governance, Financial Performance, Rural Banks.
Client Happiness as a Predictive Economic Variable in Revenue Systems
In the turbulent environment of the contemporary revenue management systems, the conventional client satisfaction indicators, such as Net Promoter Scores, are less predictive as they do not rely on prospective behavioral factors. This study redefines client happiness as an economic variable with measurement, and puts forward a new composite measure, the Emotional Continuity Index (ECI) a new model of emotionally stable, which predicts repeat engagement, quick decision making and retention. ECI is integrated into Customer Happiness Intelligence System (CHIS), a patent-pending framework based on practical executive practice, which gathers operational data through CRM systems, transaction records, and feedback systems and compiles them with the help of machine learning algorithms in real time to deliver a picture of customer satisfaction levels. The research notes the effectiveness of ECI based on a mixed-methodology which involves conceptual modeling, weighted algorithmic formulation, and 12 months of empirical validation using a stratified sample of 749 clients of Sunlocate Properties (commercial real estate sector): Results show 22% increase in revenue predictability, 15% decrease in churn, and 18% faster decision-making. Such results place client happiness as a measurable managerial dial, and this raises the strength of enterprises in complicated settings. Commercial intelligence and customer-centric design implications, which promote structured analytics to develop flexible, scalable business, can be implied. The piece of writing adds a coherent intellectual journey between the executive experience and technological innovation to close the gaps in the theoretical background and the real-life applications to make the best decisions.
Client happiness, revenue predictability, Emotional Continuity Index, commercial intelligence, customer lifecycle management, behavioral analytics
Driving the Future of Healthcare: Electric Vehicles, Data-Driven Marketing, and Financial Innovation in Pharmaceutical Value Chains
The healthcare and pharmaceutical industries are undergoing a transformative shift driven by electric vehicle (EV) adoption, data-driven marketing, and financial innovation. This paper examines how EV integration in pharmaceutical logistics enhances sustainability, reduces delivery costs, and improves supply chain resilience. It further explores how AI-powered marketing systems and blockchain-based financial mechanisms are reshaping customer engagement and operational transparency. The study synthesizes prior research to present a multi-dimensional framework linking EV infrastructure, digital intelligence, and financial optimization in healthcare value chains.
Innovation, Decision-Making, LOTAR Model, Reflective Learning, Cognitive Psychology, Qur’anic Epistemology, Creativity, Educational Management.
Internal Audit Practices, Management Controls and Fraud Prevention among Public Technical and Vocational Education and Training Institutions in Nairobi Metropolitan Area, Kenya
This study examined the effect of internal audit practices and management controls on fraud prevention among public Technical and Vocational Education and Training institutions in the Nairobi Metropolitan Area, where persistent financial mismanagement raises concerns about audit effectiveness using a descriptive research design. The study targeted 141 institutions, with 103 heads of internal audit as respondents. Primary data were collected using structured questionnaires that satisfied validity and reliability requirements. Data were analyzed using correlation and multiple regression analyses. Results from the study, which was anchored on Agency Theory, Fraud Triangle Theory, and Institutional Theory, show strong positive associations between fraud prevention and internal control systems, management controls, and internal audit IT adoption. Regression results indicate that internal audit IT adoption is the most influential predictor of fraud prevention, followed by internal control systems, both of which are statistically significant. The model explains a substantial proportion of variation in fraud prevention. The moderating effect of board oversight is not statistically significant. The study concludes that strengthening internal audit systems, particularly through digital audit adoption and robust internal controls, significantly enhances fraud prevention in public TVET institutions. Management controls further reinforce accountability.
Internal audit practices, internal control systems, fraud prevention, management controls
A Flexible Elasticity Framework for Measuring Firm Internationalization: Theoretical Model and Simulation-Based Evidence
This study develops a flexible elasticity framework for measuring firm internationalization. Unlike standard composite indices based on fixed weights, we model internationalization as a heterogeneous elasticity function in which the contribution of each dimension varies across firms. The model generalizes traditional additive approaches by introducing firm-specific elasticities that depend on observable characteristics of the firms. We provide a theoretical characterization of the model and evaluate its properties using simulations. The results show that fixed-weight indices systematically compress heterogeneity and distort firm rankings, whereas the proposed framework preserves structural variation and provides a more consistent representation of internationalization.
Examining the Origin of Innovation in Decision-Making and Proposing the LOTAR Model
In contemporary times, decision-making has become one of the most essential managerial, educational, and social competencies. While decisions occur frequently, they become meaningful and transformative only when they embody elements of creativity and innovation. The fundamental question, therefore, is: Where does innovation in human decision-making originate, and what activates this process within the individual? This study, entitled “Examining the Origin of Innovation in Decision-Making and Proposing the LOTAR Model,” aims to provide a scientific and integrative explanation of innovative decision-making by drawing on cognitive theories and Qur’anic epistemology. Inspired by these principles and integrated with established cognitive frameworks, the proposed LOTAR Model introduces a five-stage dynamic process consisting of Listening, Observation, Thinking, Action, and Reflection. The model argues that innovation does not arise from sudden inspiration, but from the continuous interaction among perception, cognition, experience, and reflective learning. A controlled field experiment was conducted within the Department of Management at Takhar University using a real educational task—improving classroom cleanliness. The results indicated that the LOTAR framework enhances innovation by strengthening Thinking, Action, and Reflection. The findings demonstrate that LOTAR provides a scientifically grounded, culturally contextualized, and Qur’an-informed approach for enhancing reflective thinking and innovative decision-making.
Investigating Effective Factors in Application of the Artificial Intelligence in Iran's Food Industry and its Effects on Employment
The fourth industrial revolution has brought tremendous changes with the introduction of artificial intelligence to various industries. In this study, the effective factors in the use of artificial intelligence in Iran's food industry and its effects on employment have been identified. Due to the growing need to improve the quality of food products, increase productivity, and reduce costs, the use of new technologies, including artificial intelligence, in this industry seems inevitable. This research examines the opportunities and challenges in the use of artificial intelligence and analyzes its effects on employment in Iran's food industry. Investigating these factors in Iran's food industry is of great importance and necessity, and increases productivity, changes in employment, competitiveness, research and development, environmental sustainability, improves food security, and develops appropriate policies. The general goal of investigating these effective factors in the use of artificial intelligence in Iran's food industry is to create a stable and innovative ecosystem that will lead to the improvement of product quality, increase employment, and the realization of sustainable development goals. The type of this research in terms of practical purpose, in terms of descriptive-analytical execution method, and in terms of data collection using studies published until 2023 using PubMed, Cochrane, SCOPUS databases, and Google Scholar search engine with key related words have been searched. This research was selected after a complete review of 30 relevant articles that were consistent with the purpose of the present study. The results show that while artificial intelligence can reduce some traditional jobs, it also creates new job opportunities and increases the need for new skills. In this research, we found that workforce skills and technological infrastructure are of higher importance, government support and organizational culture are of high importance, and costs and benefits are of medium importance, and each of them creates its own effects on employment. Finally, this research provides recommendations to policymakers and the food industry to facilitate the transition process towards artificial intelligence.
Employment, Iran, Artificial Intelligence, Food Industry, Productivity, New Technologies
Redefining Healthcare Consumption: AI-Driven Buyer Persona and Financial Transformation
Artificial Intelligence (AI) is revolutionizing the healthcare industry by reshaping consumer behavior and transforming financial structures. This study examines the evolution of the future healthcare consumer buying persona and evaluates the financial implications of AI integration. The findings suggest that AI-driven personalization, predictive analytics, and digital engagement tools significantly influence consumer decision-making while enhancing operational efficiency and reducing costs for healthcare providers. Additionally, technologies such as blockchain and digital twin systems contribute to transparency and supply chain optimization. The study concludes that AI is fostering a more consumer-centric and financially sustainable healthcare ecosystem.
Artificial Intelligence, Healthcare Consumer Behavior, Financial Impact, Digital Health, Personalization, Predictive Analytics
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Intelligent Pay-by-Bank Architecture: Fraud-Aware Analytics and Risk Modeling for U.S. Account-to-Account Payment Systems
Pay-by-bank is moving from a niche alternative to a strategically important layer in the U.S. payments landscape. Its appeal is clear: direct account-to-account (A2A) transfers can reduce merchant acceptance costs, accelerate funds availability, support recurring and bill-payment use cases, and create a pathway for open-banking-enabled checkout experiences that do not depend on card rails. At the same time, the same structural strengths that make pay-by-bank attractive—irrevocability, speed, richer data exchange, API connectivity, and multi-party orchestration—also create distinctive fraud, operational, and consumer-protection risks. Unlike card networks, many pay-by-bank implementations cannot rely on mature chargeback mechanisms as a primary safety valve, which makes ex ante risk scoring, identity assurance, behavioral analytics, and post-transaction monitoring more important. This paper develops a framework for an intelligent pay-by-bank architecture for the United States, integrating fraud-aware analytics, explainable machine learning, graph-based entity resolution, sanctions and AML screening, and rail-aware control design across ACH, Same Day ACH, RTP, and FedNow-enabled flows. Using official U.S. sources and contemporary scholarly research, the paper synthesizes the structural drivers of A2A growth, maps the major fraud typologies that threaten consumer and merchant adoption, and proposes a layered analytic design that joins customer risk, payment context, device and channel telemetry, open-banking consent events, account validation, and network intelligence into a unified decision stack. The paper also advances a practical governance model centered on explainability, model-risk management, fairness, vendor oversight, and escalation workflows aligned to high-stakes payment decisions. The discussion argues that successful U.S. pay-by-bank adoption depends not only on rail availability but on trusted orchestration: institutions must combine real-time controls, adaptive thresholds, human review for ambiguous cases, and continuous learning from returns, disputes, fraud reports, and customer complaints. The manuscript concludes that intelligent pay-by-bank systems can materially improve the safety and efficiency of U.S. A2A payments when technical architecture, controls, and governance are designed together rather than sequentially.
Pay-by-bank, account-to-account payments, ACH, Same Day ACH, FedNow, fraud analytics, explainable AI, open banking, synthetic identity, U.S. payment systems
Clustering Capital Management Approaches: Evidence from Banking Firms during Economic Disruptions
The COVID-19 pandemic significantly disrupted global markets, supply chains, and business operations, leading to adverse effects on firm performance and economic stability. The banking sector, in particular, was compelled to adapt its capital management strategies to remain resilient amid financial uncertainty. Effective capital management—encompassing efficiency, liquidity, and solvency—became critical, as poor financial decisions during crisis periods can threaten organizational survival. This study aims to examine how banking firms respond to financial uncertainty through their capital management practices and to determine how these responses relate to firm profitability. Specifically, it seeks to identify distinct patterns of capital management and assess their implications for financial performance. A quantitative research design was employed using survey data from bank representatives. K-Means Cluster Analysis, utilizing the Lloyd algorithm, was applied to classify firms based on efficiency, liquidity, and solvency. The findings revealed three distinct clusters: efficient-solvent, high-liquid, and safe-player. Results further indicate a significant relationship between capital management approaches and profitability, highlighting the importance of balancing efficiency, liquidity, and solvency. This study contributes to the literature by offering an integrated clustering perspective on capital management and provides practical insights for banking institutions to develop more resilient financial strategies during periods of economic uncertainty.
Capital management, business uncertainty, profitability, efficiency, liquidity, solvency
Modeling the Consumer Transition from Cash to Digital Payments and Its Implications for Financial System Efficiency, Transparency, and Inclusion
This study develops a quantitative framework for modeling the consumer transition from cash to digital payments and for evaluating how that transition affects efficiency, transparency, and financial inclusion. The paper combines evidence from the Federal Reserve’s Diary and Survey of Consumer Payment Choice, the World Bank’s Global Findex Database, Bank for International Settlements research on fast payment systems, and related studies published. The empirical motivation is clear: cash remains important, but U.S. consumers increasingly rely on cards, mobile phones, online banking, and account-to-account rails for purchases, bills, and person-to-person transfers. To interpret this shift, the paper specifies a multi-stage adoption framework in which consumers select payment instruments according to transaction size, acceptance, security, convenience, costs, record keeping, liquidity constraints, and digital access. A transition index and payment-efficiency score are linked to transparency and inclusion outcomes through a comparative design and a U.S.-focused calibration exercise. The results show that movement from cash toward digital payments is neither linear nor uniform. Digital migration is fastest where transaction records, remote commerce, and instant settlement offer visible utility gains. Yet adoption slows for cash-dependent households, privacy-sensitive users, the underbanked, and populations with weaker access to smartphones, broadband, or low-fee accounts. The analysis indicates that payment digitalization improves speed, traceability, and operating efficiency, but inclusion gains depend on governance, affordability, fraud protection, and user-centered design. The main policy implication is that an efficient payment future is not cashless by default; it is plural, interoperable, transparent, and resilient.
Cash, digital payments, financial inclusion, payment systems, consumer behavior, cards, mobile payments, fast payments, transparency, payment efficiency
AI-Driven Predictive Modeling for Detecting Suspicious Trading Patterns, Anomalous Order Activity, and Market Manipulation in U.S. Equity Markets
Modern U.S. equity surveillance increasingly depends on predictive systems that flag suspicious order placement, cross-venue quote behavior, and abnormal execution patterns before manipulative episodes fully unfold. This paper develops an AI-driven research framework for detecting suspicious trading patterns, anomalous order activity, and market manipulation in U.S. equity markets by combining market microstructure theory, public regulatory evidence, and contemporary machine-learning methods. Drawing on more than forty professional sources, the study positions surveillance as a multimodal prediction problem spanning limit-order-book dynamics, order-routing behavior, venue fragmentation, trader interaction networks, news and disclosure context, and enforcement-informed labels. Market-structure evidence shows that U.S. equity average daily volume rose from an estimated 11.1 billion shares in 2023 to 12.2 billion in 2024 and 17.6 billion in 2025, while off-exchange Trade Reporting Facility share increased from about 45% in April 2023 to 47.0% in 2024 and 50.6% in 2025, underscoring the scale and fragmentation challenge facing surveillance teams. The paper proposes hybrid architecture that integrates gradient-boosted baselines, temporal transformers, and graph neural networks with explainable AI overlays for analyst review. Methodologically, the framework emphasizes severe class imbalance, regime shifts, weak and delayed labels, and cost-sensitive evaluation anchored in precision at alert budgets, time-to-detection, and economic materiality rather than headline accuracy alone. The paper argues that trustworthy surveillance requires predictive power, governance discipline, auditable features, and human escalation aligned with U.S. market-structure reforms. The result is a blueprint for more adaptive and useful market-abuse detection.
market manipulation, spoofing, layering, suspicious trading, anomalous order activity, graph neural networks, market surveillance, U.S. equities, explainable AI, weak supervision
Socio-Economic Impacts and Fiscal Vulnerabilities Following the February 6, 2023 Kahramanmaraş Earthquakes in Türkiye
The two major earthquakes (Mw 7.7 and Mw 7.6) that occurred on 6 February 2023, centered in Kahramanmaraş, produced profound socio-economic impacts in southeastern Türkiye. The disaster directly affected eleven provinces, caused widespread destruction, and resulted in more than 50,000 fatalities, while also leading to significant losses in Syria. Approximately 14 million people were directly affected by the physical destruction, and long-term socio-economic consequences are expected to persist. This study evaluates the direct and indirect economic losses caused by the earthquakes within a comprehensive framework, focusing on their impact on production, foreign trade, labor markets, and public finance. In this context, the Adaptive Regional Input–Output (ARIO) model, which enables time-dependent analysis of post-disaster economic impacts, is employed. The findings indicate that the earthquakes weakened regional production capacity, increased employment losses, and significantly expanded public expenditures, thereby deepening fiscal vulnerabilities. Population mobility has also led to structural changes in labor markets and national economic balances. Although reconstruction activities may support short-term recovery, sustainable growth requires integrated policy strategies that strengthen fiscal resilience, restore employment, and enhance long-term regional economic resilience and stability.
Socio-economic impacts, fiscal resilience, ARIO model, economic vulnerability
AI-Driven Predictive Analytics Framework for Anti-Money Laundering Risk Management and Financial Infrastructure Protection in U.S. Banking Systems
U.S. banks run anti money laundering (AML) programs that must detect suspicious activity at scale, document decisions, and file timely Suspicious Activity Reports (SARs) while protecting SAR confidentiality (31 C.F.R. § 1020.320, 2023; FFIEC, 2021). Illicit finance increasingly exploits payment rails and multi account networks, raising compliance exposure and operational risk (FinCEN, 2021). This manuscript proposes a compliance aware, AI driven predictive analytics framework for U.S. banking. The framework integrates three layers: supervised alert ranking and suppression using calibrated gradient boosted trees (Chen & Guestrin, 2016); unsupervised anomaly detection for emerging typologies under label lag (Liu et al., 2008); and graph learning to capture relationship centered laundering structures such as mule rings and layering (Kipf & Welling, 2017; Hamilton et al., 2017). We specify a data and feature pipeline for deposits, wires, ACH, cards, and instant payments, including preprocessing, entity resolution, feature engineering, and class imbalance controls using cost sensitive objectives and precision recall analysis (Elkan, 2001; Saito & Rehmsmeier, 2015). Explainability is embedded as an evidence packet for investigators and validators using SHAP, LIME, counterfactuals, and graph explanations (Lundberg & Lee, 2017; Ribeiro et al., 2016; Wachter et al., 2018; Ying et al., 2019). Privacy preserving options, including federated learning, secure aggregation, and differential privacy, enable cross portfolio learning without centralizing sensitive customer data (Dwork et al., 2006; McMahan et al., 2017). Deployment guidance addresses real time scoring, latency targets, drift monitoring, and integration with case management and BSA e filing.
i-Money Laundering, Machine Learning, Graph Neural Networks, Financial Crime, National Security, Explainable AI, Privacy-Enhancing Technologies
Design and Implementation of an AI-Augmented Autonomous Financial Operations Framework for Cloud-Native ERP Systems Using SAP BTP and RAP
Enterprise financial systems are undergoing rapid transformation due to increasing regulatory complexity, high-volume digital transactions, and the shift toward cloud-native architectures. Traditional ERP financial modules rely heavily on rule-based validations and manual reconciliation processes, limiting their ability to detect anomalies, prevent revenue leakage, and adapt dynamically to evolving compliance requirements. Despite the robustness of platforms such as SAP S/4HANA, financial operations in large enterprises continue to depend on static validations, post-period reconciliation, and manually supervised exception handling. These constraints increase fraud exposure, revenue leakage risk, and financial close cycle duration. This research introduces the Autonomous Financial Operations Framework (AFOF), a cloud-native, AI-augmented architecture that embeds intelligent automation directly within ERP transactional workflows. The framework integrates: The extensibility and microservices capabilities of SAP Business Technology Platform Behavior-driven service modeling via SAP ABAP RESTful Application Programming Model (RAP) Embedded machine learning services for anomaly detection and predictive analytics The proposed framework leverages the extensibility of SAP S/4HANA, the cloud capabilities of SAP Business Technology Platform, and the service-oriented programming paradigm of SAP ABAP RESTful Application Programming Model (RAP) to create a self-optimizing financial operations layer. The architecture introduces five core components: An Intelligent Posting Validation Engine using behavior-driven RAP logic. An AI-based Anomaly Detection Module for financial irregularities. Automated reconciliation services for high-volume subledger environments. Predictive revenue leakage analytics tailored for subscription-based monetization systems. A secure event-driven extension layer supporting scalable enterprise integration. A controlled enterprise-scale simulation was conducted using synthetic financial datasets modeling high-volume subscription billing environments (10–50 million monthly transactions). Comparative benchmarking against traditional rule-based ERP controls demonstrated: 43% reduction in financial close cycle duration 37% improvement in anomaly detection precision 52% reduction in manual reconciliation effort 28% decrease in revenue leakage exposure 31% faster exception resolution turnaround time Latency measurements confirmed that embedded AI validation introduced less than 8 ms average transactional overhead, preserving ERP performance integrity. Unlike conventional ERP enhancements that operate as external monitoring tools, the AFOF embeds machine learning–assisted controls directly within transactional workflows. This approach enables real-time compliance validation, adaptive financial risk scoring, and automated exception resolution, significantly reducing operational overhead and financial exposure. Performance modeling demonstrates measurable improvements in financial close cycle time, anomaly detection accuracy, and reconciliation efficiency when compared to legacy rule-based systems. This research contributes a scalable and reusable architectural model for AI-enabled financial automation in regulated industries, including telecommunications, digital commerce, healthcare billing and large-scale enterprise services. By integrating intelligent automation within mission-critical ERP systems, this research advances enterprise cybersecurity resilience, financial governance, and digital economic infrastructure modernization.
Design; Implementation; AI-Augmented Autonomous Financial Operations Framework; Cloud-Native ERP Systems; SAP BTP; RAP
Generative AI and U.S. Financial Reporting Integrity: Detecting Narrative Manipulation, Risk Disclosure Gaming, and Fraud Signals in 10-K Filings
U.S. capital markets rely on high-integrity corporate disclosure, yet the narrative portions of annual reports—particularly Management’s Discussion and Analysis (MD&A) and Risk Factors—remain vulnerable to strategic language management and, increasingly, generative-AI-assisted drafting. Unlike traditional misstatement detection that focuses on accounting ratios, narrative manipulation can distort investor beliefs through selective emphasis, obfuscation, boilerplate recycling, and inflated forward-looking language while remaining difficult to audit at scale. This paper develops a conceptual–methodological framework to measure disclosure integrity and detect narrative manipulation in U.S. 10-K filings. We construct a transparent EDGAR pipeline that parses MD&A and Risk Factors and extracts auditable features capturing (i) financial-context tone and uncertainty, (ii) readability and complexity, (iii) risk-factor novelty versus boilerplate drift (year-to-year similarity), (iv) forward-looking intensity, and (v) “tone–fundamentals gaps” that flag potential inconsistency between narrative claims and contemporaneous performance signals. We benchmark interpretable statistical models (logit/elastic net) against flexible machine-learning specifications while preserving explainability, and we evaluate performance using governance-relevant outcomes including enforcement-linked misstatement events and restatement proxies, complemented by market-based reactions around filing dates. We further test whether narrative-risk signals strengthen in the period associated with broad diffusion of generative AI writing tools. The proposed framework contributes to accounting, finance, and regulatory technology by converting narrative disclosure risk into measurable, monitorable indicators. Findings are positioned to inform SEC disclosure oversight, audit planning, and issuer governance by enabling scalable detection of narrative manipulation and risk-disclosure gaming that may undermine investor protection and U.S. market integrity.
10-K; MD&A; risk factors; disclosure integrity; textual analysis; narrative manipulation; generative AI; SEC enforcement; audit analytics; market integrity
FinTech for Climate Resilience: Measuring Insurance Gaps, Mortgage Stress, and Household Credit Risk in the United States
Climate-related hazards are increasingly transmitted into household balance sheets through the availability, affordability, and design of homeowners insurance. When premiums rise sharply, deductibles widen, or policies are non-renewed, households face larger out-of-pocket repair burdens and liquidity shocks that can elevate mortgage delinquency and broader consumer credit stress. These dynamics are not only a household welfare concern but also a national-interest issue for U.S. housing finance resilience: persistent insurance gaps can weaken loan performance, intensify regional housing volatility, and strain servicing and loss-mitigation capacity. This paper proposes a FinTech-oriented measurement and governance framework for climate resilience that links (i) county-level hazard exposure, (ii) insurance market frictions and coverage gaps, and (iii) mortgage and household credit outcomes in USA. Conceptually, the framework treats insurance gaps as a core mechanism of climate-to-credit transmission while positioning digital finance tools—InsurTech risk signals, embedded insurance/repair financing, and data-driven hardship identification—as levers to reduce amplification and improve consumer protection. Methodologically, we outline a public-data empirical strategy combining dynamic event studies around major hazard years with difference-in-differences designs exploiting heterogeneous insurance-supply tightening (e.g., non-renewal intensity and premium acceleration). We specify a replicable construct set and proxy map based on U.S. public sources (hazard indices, insurance market indicators, HMDA origination structure, house price dynamics, and consumer stress/complaint measures) and propose robustness checks addressing pre-trends, regional confounders, and distributional heterogeneity. To make the manuscript submission-ready, we include clearly labeled demonstration results generated on a simulated county-year panel calibrated to plausible ranges; these are placeholders that can be replaced with empirical estimates once the merged dataset is assembled. Policy implications focus on governance baselines for InsurTech-driven risk signals, transparency standards for insurance frictions, targeted consumer protections, and resilience metrics for housing finance intermediaries. The paper contributes a hybrid conceptual–methodological template enabling regulators, lenders, insurers, and FinTech platforms to measure and mitigate climate-driven insurance gaps before they translate into mortgage distress and broader household credit fragility.
climate resilience; InsurTech; homeowners insurance; non-renewals; mortgage delinquency; household credit risk; consumer protection; financial stability; governance; U.S. housing finance